Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Authors: Feilong Tang, Zhongxing Xu, Ming Hu, Wenxue Li, Peng Xia, Yiheng Zhong, Hanjun Wu, Jionglong Su, Zongyuan Ge
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the Multi-Organ Segmentation Challenge dataset demonstrate that our proposed method outperforms state-of-the-art methods, highlighting its efficacy in medical image segmentation tasks. We evaluate our proposed method on Automatic Cardiac Diagnosis Challenge (ACDC) (Bernard et al. 2018) and the Synapse multi-organ segmentation (Landman et al. 2015) under various semi-supervised settings, where our method achieves state-of-the-art performances. Experimental results on benchmark datasets demonstrate that our method significant improves upon the efficacy of previous state-of-the-art methods. |
| Researcher Affiliation | Academia | 1AIM Lab, Faculty of IT, Monash University 2Xi an Jiaotong-Liverpool University 3UNC-Chapel Hill |
| Pseudocode | No | The paper describes the methodology using textual explanations, equations (e.g., Eq. 1, 2, 3, 4, 5, 9, 10), and figures (e.g., Figure 2: Overview of the proposed unified learning framework), but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository. The phrase "More details are in the appendix" does not specify if code is included there. |
| Open Datasets | Yes | We evaluate our proposed method on four public datasets with different imaging modalities, the Automatic Cardiac Diagnosis Challenge dataset (ACDC) (Bernard et al. 2018) and Synapse Dataset (Landman et al. 2015). |
| Dataset Splits | Yes | Table 1: Comparison with state-of-the-art methods on the ACDC dataset (with 5% and 10% label data) and Synapse dataset (with 10% and 20% label data). Metrics reported the mean standard results with three random seeds. Scans used Metrics Labeled Unlabeled ACDC database: 3(5%) 67(95%) 7(10%) 63(90%) Synapse dataset: 2(10%) 18(90%) 4(20%) 16(80%) |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU specifications, or memory amounts used for running the experiments. It only mentions general training parameters. |
| Software Dependencies | No | The paper mentions using the SGD optimizer but does not specify version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | All models are trained with the SGD optimizer, where the initial learning rate is 0.01, momentum is 0.9 and weight decay is 10^-4. The network converges after 30,000 iterations of training. An exception is made for the first 1,000 iterations, where λcross and λCL are set to 1 and 0, respectively, which prevents model collapse caused by the initialized prototypes. Empirically, the hyperparameter Nq (the number of anchors per class in each mini-batch) is set to 256. For each anchor, the number of positive keys N+p and negative keys Np are both set to 512. The temperature coefficient τ in Eq. 10 is set to 0.4. |